Suggestions Addressed

Renamed to SFT
Added 2 other models.
Testing required.

finetrainers/args.py

@@ -455,7 +455,7 @@ def _add_training_arguments(parser: argparse.ArgumentParser) -> None:
         "--training_type",
         type=str,
         default=None,
-        help="Type of training to perform. Choose between ['lora','full_finetune']",
+        help="Type of training to perform. Choose between ['lora','sft']",
     )
     parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
     parser.add_argument(

finetrainers/cogvideox/cogvideox_lora.py

@@ -325,3 +325,18 @@ COGVIDEOX_T2V_LORA_CONFIG = {
     "forward_pass": forward_pass,
     "validation": validation,
 }
+
+COGVIDEOX_T2V_SFT_CONFIG = {
+    "pipeline_cls": CogVideoXPipeline,
+    "load_condition_models": load_condition_models,
+    "load_latent_models": load_latent_models,
+    "load_diffusion_models": load_diffusion_models,
+    "initialize_pipeline": initialize_pipeline,
+    "prepare_conditions": prepare_conditions,
+    "prepare_latents": prepare_latents,
+    "post_latent_preparation": post_latent_preparation,
+    "collate_fn": collate_fn_t2v,
+    "calculate_noisy_latents": calculate_noisy_latents,
+    "forward_pass": forward_pass,
+    "validation": validation,
+}

finetrainers/hunyuan_video/hunyuan_video_lora.py

@@ -358,3 +358,17 @@ HUNYUAN_VIDEO_T2V_LORA_CONFIG = {
     "forward_pass": forward_pass,
     "validation": validation,
 }
+
+HUNYUAN_VIDEO_T2V_SFT_CONFIG = {
+    "pipeline_cls": HunyuanVideoPipeline,
+    "load_condition_models": load_condition_models,
+    "load_latent_models": load_latent_models,
+    "load_diffusion_models": load_diffusion_models,
+    "initialize_pipeline": initialize_pipeline,
+    "prepare_conditions": prepare_conditions,
+    "prepare_latents": prepare_latents,
+    "post_latent_preparation": post_latent_preparation,
+    "collate_fn": collate_fn_t2v,
+    "forward_pass": forward_pass,
+    "validation": validation,
+}

finetrainers/ltx_video/ltx_video_lora.py

@@ -322,7 +322,7 @@ LTX_VIDEO_T2V_LORA_CONFIG = {
     "validation": validation,
 }
 
-LTX_VIDEO_T2V_FT_CONFIG = {
+LTX_VIDEO_T2V_SFT_CONFIG = {
     "pipeline_cls": LTXPipeline,
     "load_condition_models": load_condition_models,
     "load_latent_models": load_latent_models,
@@ -335,4 +335,3 @@ LTX_VIDEO_T2V_FT_CONFIG = {
     "forward_pass": forward_pass,
     "validation": validation,
 }
-

finetrainers/models.py

@@ -1,20 +1,22 @@
 from typing import Any, Dict
 
-from .cogvideox import COGVIDEOX_T2V_LORA_CONFIG
-from .hunyuan_video import HUNYUAN_VIDEO_T2V_LORA_CONFIG
-from .ltx_video import LTX_VIDEO_T2V_LORA_CONFIG, LTX_VIDEO_T2V_FT_CONFIG
+from .cogvideox import COGVIDEOX_T2V_LORA_CONFIG, COGVIDEOX_T2V_SFT_CONFIG
+from .hunyuan_video import HUNYUAN_VIDEO_T2V_LORA_CONFIG, HUNYUAN_VIDEO_T2V_SFT_CONFIG
+from .ltx_video import LTX_VIDEO_T2V_LORA_CONFIG, LTX_VIDEO_T2V_SFT_CONFIG
 
 
 SUPPORTED_MODEL_CONFIGS = {
     "hunyuan_video": {
         "lora": HUNYUAN_VIDEO_T2V_LORA_CONFIG,
+        "sft": HUNYUAN_VIDEO_T2V_SFT_CONFIG,
     },
     "ltx_video": {
         "lora": LTX_VIDEO_T2V_LORA_CONFIG,
-        "finetune": LTX_VIDEO_T2V_FT_CONFIG,
+        "sft": LTX_VIDEO_T2V_SFT_CONFIG,
     },
     "cogvideox": {
         "lora": COGVIDEOX_T2V_LORA_CONFIG,
+        "sft": COGVIDEOX_T2V_SFT_CONFIG,
     },
 }
 

finetrainers/trainer.py

@@ -101,6 +101,7 @@ class Trainer:
 
         # Components list
         self.components = []
+
     def prepare_dataset(self) -> None:
         # TODO(aryan): Make a background process for fetching
         logger.info("Initializing dataset and dataloader")
@@ -155,16 +156,17 @@ class Trainer:
         self.transformer_config = self.transformer.config if self.transformer is not None else self.transformer_config
         self.vae_config = self.vae.config if self.vae is not None else self.vae_config
 
-        self.components = [self.tokenizer, 
-                           self.tokenizer_2, 
-                           self.tokenizer_3, 
-                           self.text_encoder, 
-                           self.text_encoder_2, 
-                           self.text_encoder_3, 
-                           self.transformer, 
-                           self.unet, 
-                           self.vae]
-
+        self.components = [
+            self.tokenizer,
+            self.tokenizer_2,
+            self.tokenizer_3,
+            self.text_encoder,
+            self.text_encoder_2,
+            self.text_encoder_3,
+            self.transformer,
+            self.unet,
+            self.vae,
+        ]
 
     def _delete_components(self) -> None:
         self.tokenizer = None
@@ -204,12 +206,12 @@ class Trainer:
             if self.args.enable_tiling:
                 self.vae.enable_tiling()
 
-    def _disable_grad_for_components(self, components:list):
+    def _disable_grad_for_components(self, components: list):
         for component in components:
             if component is not None:
                 component.requires_grad_(False)
 
-    def _enable_grad_for_components(self, components:list):
+    def _enable_grad_for_components(self, components: list):
         for component in components:
             if component is not None:
                 component.requires_grad_(True)
@@ -262,11 +264,13 @@ class Trainer:
         self._set_components(condition_components)
         self._move_components_to_device()
 
-        self._disable_grad_for_components(components=[
-            self.text_encoder,
-            self.text_encoder_2,
-            self.text_encoder_3,
-        ])
+        self._disable_grad_for_components(
+            components=[
+                self.text_encoder,
+                self.text_encoder_2,
+                self.text_encoder_3,
+            ]
+        )
         if self.args.caption_dropout_p > 0 and self.args.caption_dropout_technique == "empty":
             logger.warning(
                 "Caption dropout is not supported with precomputation yet. This will be supported in the future."
@@ -378,20 +382,22 @@ class Trainer:
         diffusion_components = self.model_config["load_diffusion_models"](**self._get_load_components_kwargs())
         self._set_components(diffusion_components)
 
-        self._disable_grad_for_components(components=[
-            self.text_encoder,
-            self.text_encoder_2,
-            self.text_encoder_3,
-            self.vae,
-        ])
-        
-        if self.args.training_type == "full_finetune":
+        self._disable_grad_for_components(
+            components=[
+                self.text_encoder,
+                self.text_encoder_2,
+                self.text_encoder_3,
+                self.vae,
+            ]
+        )
+
+        if self.args.training_type == "sft":
             logger.info("Full Fine Tuning Enabled")
             self._enable_grad_for_components(components=[self.transformer])
         else:
             logger.info("Lora Fine Tuning Enabled")
             self._disable_grad_for_components(components=[self.transformer])
-            
+
         # For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) to half-precision
         # as these weights are only used for inference, keeping weights in full precision is not required.
         weight_dtype = self._get_training_dtype(accelerator=self.state.accelerator)

train.py

@@ -4,11 +4,9 @@ import traceback
 from finetrainers import Trainer, parse_arguments
 from finetrainers.constants import FINETRAINERS_LOG_LEVEL
 
-
 logger = logging.getLogger("finetrainers")
 logger.setLevel(FINETRAINERS_LOG_LEVEL)
 
-
 def main():
     try:
         import multiprocessing